Complete treatment of uncertainties in a model for dengue R0 estimation
Complete treatment of uncertainties in a model for dengue R0 estimation
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DOI：
10.1590/s0102-311x2008000400016
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发表时间：
2008-04-01
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影响因子：
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通讯作者：
Struchiner, Claudio José
中科院分区：
文献类型：
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作者：
Coelho, Flávio Codeço;Codeço, Cláudia Torres;Struchiner, Claudio José
在真实的流行病过程中，基本再生数R-0是多个概率事件的综合结果。然而，它经常被建模为流行病学变量的确定性函数。本文讨论了在这种模式中充分处理不确定性的重要性。这是通过比较两种方法的不确定性分析：蒙特卡罗不确定性分析（MCUA）和贝叶斯融合（BM）方法。将这些方法应用于一个基于昆虫学参数确定登革热R-0的模型。BM被证明提供了一个完整的治疗与模型参数的不确定性。与MCUA相比，不确定性的合并导致参数和变量的真实后验分布。BM将所有可用信息（从观测数据到专家意见）纳入其中，从而可以建设性地使用不确定性，为模型的所有组成部分生成信息性后验分布，这些组成部分作为一个集合是一致的。
In real epidemic processes, the basic reproduction number R-0 is the combined outcome of multiple probabilistic events. Nevertheless, it is frequently modeled as a deterministic function of epidemiological variables. This paper discusses the importance of adequate treatment of uncertainties in such models. This is done by comparing two methods of uncertainty analysis: Monte Carlo uncertainty analysis (MCUA) and the Bayesian melding (BM) method. These methods are applied to a model for the determination of R-0 of dengue fever based on entomological parameters. The BM was shown to provide a complete treatment of the uncertainties associated with model parameters. In contrast to MCUA, the incorporation of uncertainties led to realistic posterior distributions for parameter and variables. The incorporation, by the BM, of all the available information, from observational data to expert opinions, allows for the constructive use of uncertainties generating informative posterior distributions for all of the model's components that are coherent as a set.